How Featherless AI Reached $3M in Annual Revenue Serving 10,000 Customers with Zero Paid Marketing (Interview with CEO Eugene Cheah)

Opening Snapshot: Revenue and Customer Count

Nathan Latka

00:00So you can see RWKB here inside of now featherless, but what you're saying is you effectively were doing all the research here. You built this featherless for yourself. What got you the first bump of sign ups?

Eugene Cheah

00:09>> So when we did the first soft launch, we had a few members of the community just posted on Reddit, essentially.

Nathan Latka

00:14Are you comfortable sharing your monthly revenue today? Is it more like 500,000 a month?

Eugene Cheah

00:18>> It's something that we are scaling up towards, and we are negotiating, like, multimillion dollar contracts on annual basis.

Nathan Latka

00:24How many customers are you serving now today?

Eugene Cheah

00:26>> So we are serving around 10,000 plus customers.

Nathan Latka

00:29So just to be clear, we're recording in April 2026. You're doing more than $250,000 a month, but less than 500,000 a month. You think you'll pass $500,000 a month sometime in fall here of twenty twenty six. Yeah. What's the largest customer paying you today?

Eugene Cheah

00:43>> So currently the biggest would be around 1 to $2,000,000 a year.

Introduction to Featherless AI and Eugene Cheah

Nathan Latka

00:47Hey, folks. My guest today is Eugene He's the CEO and co founder of featherless AI, the largest open source LLM inference provider on Hugging Face. He's offering serverless access to over 6,700 models on a flat rate pricing model that cuts inference costs by at least 10X. He co created RWKV, the first attention free AI architecture under the Linux Foundation. Eugene, you ready to take us to the top?

Eugene Cheah

01:10>> Yeah. When you look at this fundamental piece of technology called AI, it's something that we believe that shouldn't be controlled by only a handful of companies where they can choose to restrict your access and what you do with AI. So because of that, we fundamentally believe that people shouldn't be able to make their own choices and decision when accessing AI models. And the best way to do it is to support all of them in the open

01:35>> source ecosystem. And that's why we built featherless AI to support any AI model that you have on Hugging Face and to provide instant access to all of them.

What Featherless Does: Open Source Model Access Explained

Nathan Latka

01:43And so here's a list of a lot of those models. For the nontechnical listener that's using right now, can you dumb this down for a second? Explain it like you're explaining it to a kindergartner.

Eugene Cheah

01:52>> So AI models can be used for various use cases. You have a typical ChatGPT use case. You can have the the AI use cases for supporting people in particular language or domains. So for example, there there are AI models specifically tuned for the use of providing agriculture advice for farmers in both the Asia region and also a few models specifically for the North American Okay.

Nathan Latka

02:15So give me an example of how your mom would use this or how how this enables someone to build a model or a version of ChatGPT maybe that your mom could use.

Eugene Cheah

02:24>> So they could use existing applications or even go to featherless AI's inbuilt application as well. We have a chat application at the top there, and they could actually assess one of the many models that we have in our catalog. And this includes some of the models that that we have highlighted respectively. Usually, what most people will what we see from the community is that, like, be it through the redates or local communities that people will actually

02:46>> find their own preferred model, and they'll know the the models that they would like to run, and they will actually come to our platform. And they'll make the request, and we'll add support for it, and and then they can run it, should we?

Nathan Latka

02:56So let's use the it's sort of right now downloads high to low. Is this pronounced Quen? Quen ranks number one in terms of number of downloads?

Eugene Cheah

03:02>> Yeah. This is one of the most popular downloaded models. So when the users signs up on the platform, they can have access to any of these these models. So Quan, in particular, is one of the latest models made by Alibaba, and it's strong in both English and Chinese and and a few other, 100 additional languages.

Nathan Latka

03:18You guys can read the description of sort of what it does up here. So let's keep going down the stack here, Eugene. My goal on this interview is to help take a very technical sort of concept and help my listeners understand why will you build up featherless.ai so important in terms of the the the end value. Right? So are mostly or or is it mostly developers that are paying you for featherless?

Eugene Cheah

03:36>> Yeah. So developers we also see an increased wave of what I call prosumers, Entuist who's, like, not exactly purely developers. Some of them will be, like, coming in and it's like, I want to run my cloud code or I I'm or I'm white coding my apps, and and my apps run on AI. So these are the two major categories. Traditional developers who are building apps that uses AI, and these groups are and and to assist you.

Nathan Latka

03:59I have multiple different price points here, but what would you say the average customer is paying you per month today?

Pricing Model: $25 Entry Plan to Enterprise Contracts

Eugene Cheah

04:04>> So the average entry level customer is paying out the $25 a month plan. That provides them access to any other model where they can make unlimited requests, limited to one request at a time. And then subsequently, once they figure out which models they want and sometimes when they create apps and ship it to the app store, that's where they go to our scale up plan, and that's where they have much larger dedicated capacity because you don't

04:26>> really want your production access.

Nathan Latka

04:27Who's paying for the credits though there? If they pay you 25 a month and then they use some of the models that you help them sort of play with and then it gets used a bunch, who's paying for those?

Eugene Cheah

04:35>> Correct. Like so for example, like, how Heroku or Vaseo abstract the whole infrastructure layer for just deploying applications, we are abstracting that for AI models. And if you want to host and run at larger scale, we also provide the pricing plans for it.

Nathan Latka

04:49I see. Very cool.

04:50>> Okay. It's now making sense to me, and I'm not a tech person. So I imagine my audience is now following along nicely. How many customers are you serving now today? And then we'll get your backstory here.

Eugene Cheah

05:00>> Yeah. So we are serving around 10,000 plus customers, and the I would jokingly say the average customers do not know what b 200, m I three two five. They may have heard of h 100 because it was in the news, and that's essentially what our job is. We abstract away all the complexity of running these AI models and the infrastructure for it. So and the this growing collection of open models include some of the best models

05:24>> that are already on par or surpassed in, let's say, CloudsOnet or even the GPT for our mini. And we realized, actually, for a lot of customers, when they move to production, these are the models that are more than sufficient for their inventory.

Nathan Latka

05:36Eugene, tell me more of your backstory here. When did you write the first line of code for featherless? What year?

Origin Story: From Recurso Pricing Experiment to Featherless

Eugene Cheah

05:42>> So featherless started out kind of like as an accident that outgrew itself two years ago. So as you heard, we started from the RWKB committee where we were doing experiments in next generation foundation models. And and that's where our roots are. Like, this new AI architecture has the potential of reducing inference costs by over a thousand x. And if you feel all the energy demands,

06:06>> that is extremely well.

Nathan Latka

06:07What was the first massive sort of sign up surge that you saw? Was it a article on on Hacker News or somewhere else? What got or Reddit, what got you the first bump of sign ups?

First Growth Surge: Reddit and the Long Tail of Models

Eugene Cheah

06:17>> So when we did the first soft launch, we actually just we had a few members of the committee just posted on on Reddit essentially to some of the existing AI committees because we knew that we wanted to to serve the models that no one else hosted. And that's what really drove the traffic. You see most providers, they only provide, let's say, less than a 100 models. That covers 50% of our inference workload. It's the bottom 50%

06:41>> where they run all these interesting fine tuned models that people came on board for. And they are all usually very unique use cases or languages.

Nathan Latka

06:51Is this you? Is this your silly Tavern AI? Is that you?

Eugene Cheah

06:54>> It's one of our, yes, the staff members that that probably did the initial post.

Nathan Latka

06:58How many co founders do you have?

Eugene Cheah

07:00>> We have three founders. Yeah. So this is probably it was done by Wes. So Wes, Wesley George was, our COO. He's based in Toronto. And Harrison Vanderveld, he's based in Australia, our CTO.

Nathan Latka

07:13Okay. Wow. So 2024 was official launch then two years ago. And you're doing more than $250,000 a month today in revenue. So it's fair to say you've gone from zero to a million dollars of revenue, what, like, very quickly, right, in a couple months?

Eugene Cheah

07:25>> Yes.

Nathan Latka

07:26You're like, you're an engineer, and I'm a business guy. So it's uncomfortable when I ask you finance questions, but I love to capture the growth story.

Eugene Cheah

07:33>> It's you wanna hear the funniest bit about this. Right? It's the company at that point in time was called Recurso. So we already had an inference platform for RWKB, recursion recursive model, RWKB. It all makes sense. And featherless was meant to be a name pricing experiment, so we gave it a different name. But within the first few days, it became more profitable and more revenue than the original company platform that we were like, I guess we

08:01>> are featherless now.

Nathan Latka

08:02That's amaze so what what did it I mean, did you guys go I mean, you guys, as your cofounders,

08:06>> you must have said, oh

08:06>> my gosh. We just passed 88 $83,000 a month in revenue, and we've only been live for, like, three months.

08:11How many months did it take you to break 83,000 a month? Do you remember?

Team Structure: 27 People Across Infra, Platform, and Research

Eugene Cheah

08:15>> Oh, wow. I can't really remember that moment, but it was like it was all such a blaze because, like like, it was a case of, like, we get more users, the servers are on fire, we add more servers, we get more users, we add more servers. And so it was, like, just a constant hectic rush there. Most and it it wasn't until, like, much more recently where we had a lot we recently had closed our funding,

08:39>> our latest a round, where we had enough server capacity to like, ah, we have a sign of relief now. The the the the there's slightly more servers than users for now. For now. That's tough. I'm all ears for that, and I would like to know how many of your portfolio is burning so much AI usage that we can come in to help them lower their

Nathan Latka

08:59Guys, remember, I am not just a YouTuber. I'm investing into my third fund. We've deployed $250,000,000 into 550 software companies so far, again, at founderpath.com. If you're interested in capital, I would love to cut you a check because I know you're investing in your education. You watch my show. So sign up at founderpath.com. And when you get the onboarding email, I reply and I see all those. Just reply and say, Nathan, I found you through YouTube

Customer Acquisition: Word of Mouth vs. Enterprise Sales

Nathan Latka

09:22and I'll make sure to prioritize you. I would love to cut you a check. Check out founderpath.com. Tons. I mean, this is why it's interesting, right? When we look at all of the profit and loss, so every portfolio company has to connect their profit and loss to Founderpath. I can look in their cost of goods sold line, and I see how much they're paying to Anthropic and OpenAI and, like, on all the credit spend. If you're

09:43telling me that you've got a way to help them cut their cogs in half, right, or even more, that's extremely valuable.

Eugene Cheah

09:51>> Exactly. And that's actually how, like, our sales team are starting to close a lot of these deals because they'll come in and say, hey. Why are using AI for? Do you know, like, for half of this workload, you could use this open model that's much cheaper and lower? We can provide that. For this half of this world, you can use this model. And and since we have seen them all, we can advise more specifically.

Nathan Latka

10:12I love that. So how much of what you do would you say is sort of note people buying you sort of without emailing you, without a call versus high touch, you telling people what model they should use or how they can save 10 times or, you know, 10 x their inference spend?

Enterprise Motion: Targeting $100K per Month OpenAI Spenders

Eugene Cheah

10:24>> It's actually, it's currently a bit of a both. So when it comes to the individual users, when they're coming in on the public cloud, this is true word-of-mouth self discovery for most of the cases. And every now and then, some of these users will upgrade down the path respectively. But we also realize that there is a lot of money on the table right now where you can go after the startups that, hey, I just built my

10:47>> entire startup or SMB on OpenAI or Entropic, and I'm burning a $100,000 a month. And I do not know what I was doing. And it's like, okay. We can help you here. We we can lower your bill by half, and and and we can see where it goes. That is usually some of our most ideal large volume customers when they are basically spending that much and they are entering a situation where, hey, we need to start

11:10>> thinking about this and then we step.

Nathan Latka

11:12Okay. So here's the deal. I know you're an engineer. You're not a deal guy, but I'm gonna try and sell you. Tell me more about your team, Eugene. How many people are full time today?

Eugene Cheah

11:19>> So we have we currently have around 27. We are close to past the 30 mark soon. We are aggressively hiring. And our team is split across both the platform for Deploy Engineers, we support the customers. The infrastructure team that keeps everything running, but doesn't do anything with building, for example. And then we also have the research team that is still working on on the RWKB line of models because we still feel that there's a lot of

11:47>> room still to optimize these AI models today.

Nathan Latka

11:50How many of the 27 are working on infrastructure?

Eugene Cheah

11:54>> So around Shelf is working on infrastructure right now. And then ten ten is moving towards platform go to market. And then the rest is increasingly like GTM, like marketing activities and research.

Nathan Latka

12:09Okay. Got it. So 12 on infra, 10 on like platform and then the rest, you know, call it five, six people are sort of research and go to market motion?

Eugene Cheah

12:18>> Yes.

12:19>> The platform does do some marketing as well. Yeah. They do more like DevRel and things like that.

Nathan Latka

12:25And is all of the inbound right now pure word-of-mouth or just Reddit? Or or how are the customers finding you?

Eugene Cheah

12:32>> Both via Reddit, Hugging Face, various other platforms. We have started trying to we also have started, like, preparing, like, marketing materials, but those haven't really kicked off yet. So it's mostly been Reddit and whatnot. Yeah. Right. If you're not going to fight the giant battle of, like, the top 10 models, Quen is one of the top the top 10 and top 100 models where all the providers are fighting it out there. You're gonna see, like, eight,

12:57>> ten providers. Our strategy is we are supporting all the other models that people are interested in in experimenting. And for example, a step fund model is a particularly popular model for us that easily ship several contracts for us of this model alone. Nobody else is

Nathan Latka

13:16are you guys I mean, I know you're smart. Right? I mean, you you I can I don't understand all the research, I can see you're doing a ton of research, but why are you the only inference provider listed here? Is it just really hard to build that inference model?

Why Model Breadth Is the Moat

Eugene Cheah

13:27>> Yeah. When you talk about the top 10 or top 100 models, yes, they are. If you talk about the rest, that's where we come in. And in an AI landscape where in the try to view it the other way. Like, today, a lot of companies have started fine tuning their own models to for for their own unique use cases and specialization. In a world where various companies are fine tuning their own model, you can't go to

13:53>> a provider that can only support a 100 models. There are more than a 100 companies on earth. You want an infrastructure tailored to be able to handle all these various fine tunes.

Nathan Latka

14:02So do you have the largest coverage? I mean, is that what you measure? How many how many models can you cover?

Eugene Cheah

14:07>> Yes. And that also allows us to have a lot of demand for all these models. Like StepFund, for example, is not an unpopular model. It's shipping billions of tokens per day.

Nathan Latka

14:19Can I see that somewhere on Hugging Face?

Eugene Cheah

14:21>> Yeah. Unfortunately, I don't think Hugging Face provide that service. But if you go by the download count, it is quite a popular model. You have to understand that this is a 200,000,000,000 parameter model, meaning you need at least some of the highest end GPU. We are talking about about at least for H100. We are talking about like $20 per hour systems to run this model. And we are talking about like hundreds of thousands of companies are

14:43>> already using this model.

Nathan Latka

14:44Interesting. Very, very cool. Okay. I've learned a lot on this episode. I guess let me just ask one or two other questions. Like I know Cohere just from my background in like the SaaS space. I don't think they're technologists like you. Why do they have an inference provider option over here?

Eugene Cheah

14:58>> So Cohere in particular, they have their own particular line of models that they created for basically the North American market and in particular Canada. So they are going to the direction of highly tailored sovereign AI models for the domestic market. And we actually see this happening more and more. So for Cohere, they'll service the the Canadian market. For The US market, it's gonna be served by OpenAI and Tropic. For the French market, it's gonna be served

15:25>> by Mistral, for example. But for all the other nations who are not creating models from scratch, they are increasingly actually leaning in towards fine tuning their own specialized models for their own domestic use. And that's where we help fill in those gaps. We are not here currently like Cohere as of now. We might in the future create our own line of open models. But as of now as of now, we are trying to cater more towards

15:50>> all the other various use cases where people fine tune their

15:53>> own respective models. Yeah.

Nathan Latka

15:54Interesting. Well, this is great.

Eugene Cheah

15:56>> So Go ahead. For example, Quoteer does have contracts with the Canadian governments and so on, things like that.

Nathan Latka

16:02When you're working on large contracts as well, what's the largest cost don't name the customer obviously, but what's the largest customer paying you today? Is it like $500,000 a year or 1,000,000 a year?

Largest Customer Contract and Infrastructure Cost Reality

Eugene Cheah

16:12>> So currently the biggest will be around 1 to $2,000,000 a year, which may sound extremely large, but when you actually peel behind the layers, it only comes to around like five, six of the largest servers you see in the market.

Nathan Latka

16:25Yep. Yep. Your guys are just getting started. A lot of growth ahead, hopefully.

Eugene Cheah

16:29>> Yes. That that that's what we are rather excited.

Nathan Latka

16:32Eugene, I'm not a technologist, so I'm fully aware that there are questions I probably should have asked that I didn't. Is there anything else you wanna cover over the last two minutes here?

Research Vision: Next Generation AI Architecture

Eugene Cheah

16:40>> So the reason why we still do a lot of, research into next generation AI architecture is that we are fundamentally believe that we are still in the very early stages of AI. AI models can be much smarter, much more efficient, much smaller. And the reason why I believe in that fundamentally is I point to myself as a human. I didn't need trillions of tokens to be trained to reach university level intelligence, and I didn't need a

17:06>> trillion parameters to function here talking to you. There is something fundamentally that we can do better, and that's why we still maintain that research. And we apply that as we improve everything along the way.

Nathan Latka

17:18So, guys, if you're listening and you're spending a lot of money on credits, right, go check out featherless.ai, work with Eugene's team and figure out if there's a way that they can save you sixty, seventy, 80% of your current COGS that you can scale more efficiently. Eugene, how's that for a sales pitch?

Eugene Cheah

17:32>> Awesome. And we can and if we don't help you save, we will refund you back. That that is I what we agree

Closing: How to Follow Featherless and Eugene Cheah

Nathan Latka

17:38love that. That's recorded. So we're gonna hold you to that. But Eugene, this is great. If people wanna follow your story online, where's the best place where they can find you?

Eugene Cheah

17:45>> So they can they can follow me either on Twitter, on the handle, pico creator, p I c o, c r e a t o r, or I do also have have my own sub stack, which I maintain, tech talk CTO. Otherwise, just sign up for the featherless news like that that folks do.

Nathan Latka

18:00Guys, there you have it. Featherless.a I started off as a research lab, built a tool for themselves. It then started taking off. They have over 10,000 customers today paying between $25 a month and $100 a month on average. They're north of $250,000 a month in revenue, but under $500,000 a month in revenue. But they're really onto something. Their team is 27 people. They're investing deep in infrastructure, 12 people full time on infra, five on research, 10

18:23on platform and go to market. They've taken strategic investment from folks that can help them secure data center processing power to help their customers. That's why they've raised from folks in the industry. They've raised a 2,000,000 c seed round caught pre '20 around 2023, pre 2023, and then a big series a in December 2025 for $20,000,000 now scaling quickly. Their largest customer are already paying one to two million bucks per year as Eugene and his team

18:47starts to scale faster. Their goal is to help you again build on top of customized foundation models and decrease the cost, right, of the credits you might be currently paying to Anthropic or OpenAI. They claim they can save you up to 10 times or decrease your cost by call it 90%. So reach out featherless dot ai. Eugene, thanks for taking us to the top.

Eugene Cheah

19:04>> Thank you very much for having me here.

Nathan Latka

19:05You won't believe this CEO's revenue. Click here to watch the next episode right now.